05RestaurantReviewClassificationWithNLTK.ipynb

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      "name": "05RestaurantReviewClassificationWithNLTK.ipynb",
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      "cell_type": "markdown",
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      "source": [
        "# 1)  Importing the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xZEAlLj7_Zuw",
        "colab_type": "text"
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      "source": [
        "In this section we will classify written restorent reviews, here we will predict a review is positive or negative\n",
        "\n",
        "You can use same algorithm to other kinds of text, like books (to predict the gerne of book comedy, romance, etc), newspaper article (predicting the category of article)\n",
        "\n",
        "It is a general model that you can apply on most of the text"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxUIZpu6u66C",
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      "source": [
        "# Importing the libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "# Importing the dataset\n",
        "data = pd.read_csv('Restaurant_Reviews.tsv', sep='\\t', quoting=3)\n",
        "# quoting parameter will avoid any issue related to double quotes in reviews text\n",
        "# code to ignore double quotes is value 3"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "data.head()"
      ],
      "execution_count": 4,
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        {
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          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Review</th>\n",
              "      <th>Liked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Wow... Loved this place.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Crust is not good.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Not tasty and the texture was just nasty.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Stopped by during the late May bank holiday of...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The selection on the menu was great and so wer...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                                              Review  Liked\n",
              "0                           Wow... Loved this place.      1\n",
              "1                                 Crust is not good.      0\n",
              "2          Not tasty and the texture was just nasty.      0\n",
              "3  Stopped by during the late May bank holiday of...      1\n",
              "4  The selection on the menu was great and so wer...      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
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      },
      "source": [
        "data.tail()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Review</th>\n",
              "      <th>Liked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>I think food should have flavor and texture an...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>Appetite instantly gone.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>Overall I was not impressed and would not go b...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>The whole experience was underwhelming, and I ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>Then, as if I hadn't wasted enough of my life ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "                                                Review  Liked\n",
              "995  I think food should have flavor and texture an...      0\n",
              "996                           Appetite instantly gone.      0\n",
              "997  Overall I was not impressed and would not go b...      0\n",
              "998  The whole experience was underwhelming, and I ...      0\n",
              "999  Then, as if I hadn't wasted enough of my life ...      0"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 5
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    {
      "cell_type": "code",
      "metadata": {
        "id": "SuvDcrUFyPZk",
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      "source": [
        "# in liked column 1 means review is positive\n",
        "# and 0 means review is negative"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zy5rmU-F_-Vp",
        "colab_type": "text"
      },
      "source": [
        "# 2) Text Cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eq8brp-aJVze",
        "colab_type": "text"
      },
      "source": [
        "## Step 1: Remove numbers and punctuations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f_GbPUCfACEq",
        "colab_type": "code",
        "outputId": "a10d8a00-00b4-4a01-ddba-d04dc4d4767c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "import re # regular expression library\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2rO1sGmpEDTk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# we will apply the steps of cleaning process to first review \n",
        "# and then we will apply same to all reviews using for loop"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJKlbvKCF-H3",
        "colab_type": "code",
        "outputId": "f0ee964b-7ea6-4a26-9f1d-b98bba17111e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "data['Review'][0] # first review"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Wow... Loved this place.'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ABbcnjWF-yU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "review = re.sub('[^a-zA-Z]',' ',data['Review'][0])\n",
        "# only keeping the letters in the review, that means we will remove numbers, punctuations, etc\n",
        "# we are keeping all the smallcase and uppercase letters\n",
        "# data['review'][0] --- our first review\n",
        "# after removing unwanted charactors, the remaining charactors can form a word\n",
        "# so we will replace removed charactor by space(' ')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAOPDwhgIbS_",
        "colab_type": "code",
        "outputId": "d7280320-bca8-47cf-a14c-14a2d045420d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "print(review)\n",
        "# only letters remaining in the output"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Wow    Loved this place \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tR3eexj9JdyC",
        "colab_type": "text"
      },
      "source": [
        "## Step 2: Convert all the letters in lowercase"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F118Bo7zIe-8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "review = re.sub('[^a-zA-Z]',' ',data['Review'][0])\n",
        "review = review.lower() # convert all the letters in lowercase"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GcZdEjhTIfBT",
        "colab_type": "code",
        "outputId": "f58566c2-b155-4cc4-ec5c-706b1868eadb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "print(review) # all the letters are converted in lower case"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "wow    loved this place \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HekRlGjPKWac",
        "colab_type": "text"
      },
      "source": [
        "## Step 3: Removing the stopwords"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NzcLsGrhKaJd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# stopwords are the words,\n",
        "# that are non relevant in predicting whether a review is positive or negative\n",
        "# e.g  the, and, in, a"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RvdtFqfwKaL6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# to remove the stopwords we will use the for loop"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "furEVfxiKaOb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "review = re.sub('[^a-zA-Z]',' ',data['Review'][0])\n",
        "review = review.lower() # convert all the letters in lowercase\n",
        "review =review.split() # a single review is a string, words are not seperated\n",
        "# to gothrough all the words, we have to split the words"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBpLPoBgSSC2",
        "colab_type": "code",
        "outputId": "2ea83317-8a20-4e7e-a16c-d7535330fd4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "review\n",
        "# now this review is a list of elements"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['wow', 'loved', 'this', 'place']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CmPGMgDRSdrd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "review = re.sub('[^a-zA-Z]',' ',data['Review'][0])\n",
        "review = review.lower() # convert all the letters in lowercase\n",
        "review =review.split() # a single review is a string, words are not seperated\n",
        "# to gothrough all the words, we have to split the words\n",
        "review = [word for word in review if not word in set(stopwords.words('english'))] # taking all the words in review except stop words"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uveBcaQVSqTS",
        "colab_type": "code",
        "outputId": "f76d33db-c637-47dc-a997-398bb7893a48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "print(review) # stopwords removed"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['wow', 'loved', 'place']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hCeVTYqvb0Es",
        "colab_type": "text"
      },
      "source": [
        "## Step 4: Stemming"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "swxp6oy7bxyW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "review = re.sub('[^a-zA-Z]',' ',data['Review'][0])\n",
        "review = review.lower() # convert all the letters in lowercase\n",
        "review =review.split() # a single review is a string, words are not seperated\n",
        "# to gothrough all the words, we have to split the words\n",
        "ps = PorterStemmer() # object of class PorterStemmer\n",
        "review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))] # taking all the words in review except stop words"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlBWVdDXSqWI",
        "colab_type": "code",
        "outputId": "0cc03566-8e20-4da1-aae6-95d50bd68346",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "print(review) # stemming applies successfully to each word in review"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['wow', 'love', 'place']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RZb5Wh6ugCC3",
        "colab_type": "text"
      },
      "source": [
        "## Step 5: Join the tokens"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0QJa5ZW0e2dw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "review = re.sub('[^a-zA-Z]',' ',data['Review'][0])\n",
        "review = review.lower() # convert all the letters in lowercase\n",
        "review =review.split() # a single review is a string, words are not seperated\n",
        "# to gothrough all the words, we have to split the words\n",
        "ps = PorterStemmer() # object of class PorterStemmer\n",
        "review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))] # taking all the words in review except stop words\n",
        "review = ' '.join(review)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7CYu32DgNib",
        "colab_type": "code",
        "outputId": "fb1a0794-5f13-4871-edb7-cde421ed79f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "print(review) # now it is a string"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "wow love place\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "csksCDNNgNuZ",
        "colab_type": "code",
        "outputId": "57f29828-e4bb-447c-b861-d94f526e2768",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "type(review)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fV4CaR6Lg_wo",
        "colab_type": "text"
      },
      "source": [
        "## Step 6: Apply text cleaning to all the reviews in dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_XFDMuWhD7s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corpus = [] # define an empty list\n",
        "\n",
        "for i in range(0, 1000): # index locations 0 to 1000\n",
        "  review = re.sub('[^a-zA-Z]',' ',data['Review'][i]) # replace 0 by i \n",
        "  review = review.lower() # convert all the letters in lowercase\n",
        "  review =review.split() # a single review is a string, words are not seperated\n",
        "  # to gothrough all the words, we have to split the words\n",
        "  ps = PorterStemmer() # object of class PorterStemmer\n",
        "  review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))] # taking all the words in review except stop words\n",
        "  review = ' '.join(review)\n",
        "  corpus.append(review)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEx0yI4B4YI0",
        "colab_type": "code",
        "outputId": "85aacc4f-db90-44a0-8429-1ee1094f2e11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "print(corpus)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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'servic terribl though', 'place overpr consist boba realli overpr', 'pack', 'love place', 'say dessert yummi', 'food terribl', 'season fruit fresh white peach pure', 'kept get wors wors offici done', 'place honestli blown', 'definit would eat', 'wast money', 'love put food nice plastic contain oppos cram littl paper takeout box', 'cr pe delic thin moist', 'aw servic', 'ever go', 'food qualiti horribl', 'price think place would much rather gone', 'servic fair best', 'love sushi found kabuki price hip servic', 'favor stay away dish', 'poor servic', 'one tabl thought food averag worth wait', 'best servic food ever maria server good friendli made day', 'excel', 'paid bill tip felt server terribl job', 'lunch great experi', 'never bland food surpris consid articl read focus much spice flavor', 'food way overpr portion fuck small', 'recent tri caballero back everi week sinc', 'buck head realli expect better food', 'food came good pace', 'ate twice last visit especi enjoy salmon salad', 'back', 'could believ dirti oyster', 'place deserv star', 'would recommend place', 'fact go round star awesom', 'disbelief dish qualifi worst version food ever tast', 'bad day low toler rude custom servic peopl job nice polit wash dish otherwis', 'potato great biscuit', 'probabl would go', 'flavor perfect amount heat', 'price reason servic great', 'wife hate meal coconut shrimp friend realli enjoy meal either', 'fella got huevo ranchero look appeal', 'went happi hour great list wine', 'may say buffet pricey think get pay place get quit lot', 'probabl come back', 'worst food servic', 'place pretti good nice littl vibe restaur', 'talk great custom servic cours back', 'hot dish hot cold dish close room temp watch staff prepar food bare hand glove everyth deep fri oil', 'love fri bean', 'alway pleasur deal', 'plethora salad sandwich everyth tri get seal approv', 'place awesom want someth light healthi summer', 'sushi strip place go', 'servic great even manag came help tabl', 'feel dine room colleg cook cours high class dine servic slow best', 'start review two star edit give one', 'worst sushi ever eat besid costco', 'excel restaur highlight great servic uniqu menu beauti set', 'boyfriend sat bar complet delight experi', 'weird vibe owner', 'hardli meat', 'better bagel groceri store', 'go place gyro', 'love owner chef one authent japanes cool dude', 'burger good pizza use amaz doughi flavorless', 'found six inch long piec wire salsa', 'servic terribl food mediocr', 'defin enjoy', 'order albondiga soup warm tast like tomato soup frozen meatbal', 'three differ occas ask well done medium well three time got bloodiest piec meat plate', 'two bite refus eat anymor', 'servic extrem slow', 'minut wait got tabl', 'serious killer hot chai latt', 'allergi warn menu waitress absolut clue meal contain peanut', 'boyfriend tri mediterranean chicken salad fell love', 'rotat beer tap also highlight place', 'price bit concern mellow mushroom', 'worst thai ever', 'stay vega must get breakfast least', 'want first say server great perfect servic', 'pizza select good', 'strawberri tea good', 'highli unprofession rude loyal patron', 'overal great experi', 'spend money elsewher', 'regular toast bread equal satisfi occasion pat butter mmmm', 'buffet bellagio far anticip', 'drink weak peopl', 'order correct', 'also feel like chip bought made hous', 'disappoint dinner went elsewher dessert', 'chip sal amaz', 'return', 'new fav vega buffet spot', 'serious cannot believ owner mani unexperienc employe run around like chicken head cut', 'sad', 'felt insult disrespect could talk judg anoth human like', 'call steakhous properli cook steak understand', 'impress concept food', 'thing crazi guacamol like pur ed', 'realli noth postino hope experi better', 'got food poison buffet', 'brought fresh batch fri think yay someth warm', 'hilari yummi christma eve dinner rememb biggest fail entir trip us', 'needless say go back anytim soon', 'place disgust', 'everi time eat see care teamwork profession degre', 'ri style calamari joke', 'howev much garlic fondu bare edibl', 'could bare stomach meal complain busi lunch', 'bad lost heart finish', 'also took forev bring us check ask', 'one make scene restaur get definit lost love one', 'disappoint experi', 'food par denni say good', 'want wait mediocr food downright terribl servic place', 'waaaaaayyyyyyyyyi rate say', 'go back', 'place fairli clean food simpli worth', 'place lack style', 'sangria half glass wine full ridicul', 'bother come', 'meat pretti dri slice brisket pull pork', 'build seem pretti neat bathroom pretti trippi eat', 'equal aw', 'probabl hurri go back', 'slow seat even reserv', 'good stretch imagin', 'cashew cream sauc bland veget undercook', 'chipolt ranch dip saus tasteless seem thin water heat', 'bit sweet realli spici enough lack flavor', 'disappoint', 'place horribl way overpr', 'mayb vegetarian fare twice thought averag best', 'busi know', 'tabl outsid also dirti lot time worker alway friendli help menu', 'ambianc feel like buffet set douchey indoor garden tea biscuit', 'con spotti servic', 'fri hot neither burger', 'came back cold', 'food came disappoint ensu', 'real disappoint waiter', 'husband said rude even apolog bad food anyth', 'reason eat would fill night bing drink get carb stomach', 'insult profound deuchebaggeri go outsid smoke break serv solidifi', 'someon order two taco think may part custom servic ask combo ala cart', 'quit disappoint although blame need place door', 'rave review wait eat disappoint', 'del taco pretti nasti avoid possibl', 'hard make decent hamburg', 'like', 'hell go back', 'gotten much better servic pizza place next door servic receiv restaur', 'know big deal place back ya', 'immedi said want talk manag want talk guy shot firebal behind bar', 'ambianc much better', 'unfortun set us disapppoint entre', 'food good', 'server suck wait correct server heimer suck', 'happen next pretti put', 'bad caus know famili own realli want like place', 'overpr get', 'vomit bathroom mid lunch', 'kept look time soon becom minut yet still food', 'place eat circumst would ever return top list', 'start tuna sashimi brownish color obvious fresh', 'food averag', 'sure beat nacho movi would expect littl bit come restaur', 'ha long bay bit flop', 'problem charg sandwich bigger subway sub offer better amount veget', 'shrimp unwrap live mile brushfir liter ice cold', 'lack flavor seem undercook dri', 'realli impress place close', 'would avoid place stay mirag', 'refri bean came meal dri crusti food bland', 'spend money time place els', 'ladi tabl next us found live green caterpillar salad', 'present food aw', 'tell disappoint', 'think food flavor textur lack', 'appetit instantli gone', 'overal impress would go back', 'whole experi underwhelm think go ninja sushi next time', 'wast enough life pour salt wound draw time took bring check']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZNtQezXi2Y_",
        "colab_type": "text"
      },
      "source": [
        "# 3) Bag of Words model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0WBSAfj3hFsL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# In Bag of Words model we will take all the words in 1000 reviews\n",
        "# and here we will select unique words, no repeatition"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVAwliU1hD-n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# then we will create one column to each unique word\n",
        "# after that we will put all these columns in a table, where rows = 1000 reviews and columns = words"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3z4CrPLRAIDg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vjzY1ygx89zF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cv = CountVectorizer(max_features=1500) # keep 1500 most frequent words\n",
        "x = cv.fit_transform(corpus).toarray() # convert x into matrix using toarray method"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgcMWQFjEhda",
        "colab_type": "code",
        "outputId": "83c49b94-bed2-44c3-92ed-73733a047691",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "x.shape # 1000 reviews and 1500 most frequent words"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 1500)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5e42uKiAEM1L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# dependent variable\n",
        "y = data.iloc[:, 1].values # selecting column 'liked' as dependent variable to train ML model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J8UJTaIgF6p1",
        "colab_type": "text"
      },
      "source": [
        "# 4) Splitting the dataset into the Train and Test set\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNhXO9AYF1VJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.20, random_state = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jOaYcP-ZGVKa",
        "colab_type": "code",
        "outputId": "b4321673-34ce-4b94-d49f-c830b7fe00d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "x_train.shape, y_train.shape"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((800, 1500), (800,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elEOWKVtGctb",
        "colab_type": "code",
        "outputId": "70db1602-b805-475f-a8fd-7b100a705ad3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "x_test.shape, y_test.shape"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((200, 1500), (200,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7dvidc1FGYV-",
        "colab_type": "text"
      },
      "source": [
        "# 5) Train the model (Naive Bayes)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0pstpWjGVS4",
        "colab_type": "code",
        "outputId": "2b8bd8fd-810d-4b0e-e949-90d89d40f09f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "classifier = GaussianNB()\n",
        "classifier.fit(x_train, y_train)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB(priors=None, var_smoothing=1e-09)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWl5e7OLGupG",
        "colab_type": "text"
      },
      "source": [
        "# 6) Predict the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sOysfSIHV7O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z99s9yhaGVYF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = classifier.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zixL_uroF1Sd",
        "colab_type": "code",
        "outputId": "1311f382-9b6a-40fe-ad59-1a108f19b52e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# confusion_matrix\n",
        "confusion_matrix(y_test, y_pred)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[55, 42],\n",
              "       [12, 91]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJGTTbfdHaND",
        "colab_type": "code",
        "outputId": "5bbf7fd1-a278-4082-a6aa-77d96a18cc08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        }
      },
      "source": [
        "# classification_report\n",
        "print(classification_report(y_test, y_pred))\n",
        "# we are getting almost 73% accuracy"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.57      0.67        97\n",
            "           1       0.68      0.88      0.77       103\n",
            "\n",
            "    accuracy                           0.73       200\n",
            "   macro avg       0.75      0.73      0.72       200\n",
            "weighted avg       0.75      0.73      0.72       200\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfxL80wIHkmj",
        "colab_type": "code",
        "outputId": "411cb4da-86dc-4ea9-e4a4-4541de33a815",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "accuracy_score(y_test, y_pred)\n",
        "# 73% accuracy"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.73"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    }
  ]
}